自动化学报2018，Vol.44Issue(2)：280-288,9.DOI:10.16383/j.aas.2018.c160704
基于边缘保持滤波的高光谱影像光谱-空间联合分类
Spectral-spatial Joint Classification of Hyperspectral Image with Edge-preserving Filtering
摘要
Abstract
To deal with the problem of "curse of dimensionality" caused by high dimension and the underutilization of spatial contexture information in classification of hyperspectral images,a new spectral-spatial joint classification method based on edge-preserving filtering is proposed.The proposed method consists of the following four steps.Firstly,the hyperspectral image is divided into several subsets of bands.By extracting the principal component of each subset,a new low-dimcnsional feature set is constructed.Secondly,the pre-classification result,which is obtained by support vector machines with the new feature set,is represented as multiple initial probabilistic maps.Then edge-preserving filtering is operated on each initial probabilistic map to merge the spectral and spatial information.Finally,the class of each pixel is determined by the maximum value of the corresponding filtered probabilistic maps.The proposed algorithm is examined by the Indian Pines and Pavia University hyperspectral datasets.On the same experimental conditions,the proposed method achieves the highest classification accuracy and the lowest time consumption,demonstrating obvious advantages in hyperspectral image classification.关键词
高光谱/边缘保持滤波/支持向量机/光谱-空间联合分类Key words
Hyperspectral/edge-preserving filtering (EPF)/support vector machine (SVM)/spectral-spatial joint classification引用本文复制引用
张成坤,韩敏..基于边缘保持滤波的高光谱影像光谱-空间联合分类[J].自动化学报,2018,44(2):280-288,9.基金项目
国家自然科学基金(61773087,61374154),中央高校基本科研业务费(重点类项目)(DUT17ZD216),国家自然科学基金委科学仪器基础研究专项(51327004)资助 Supported by National Natural Science Foundation of China (61773087,61374154),Fundamental Research Funds for the Central Universities (DUT17ZD216),and Special Fund for Basic Research on Scientific Instruments of the National Natural Science Foundation of China (51327004) （61773087,61374154）
